Dynamic User Assessment Weighting for Information Module Ranking
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Solution Overview
Problem
Existing computer systems that facilitate communication and knowledge sharing among employees lack effective methods to accurately rank and display user-provided information modules, leading to inefficiencies in knowledge utilization and expertise valorization within organizations.
Innovation Solution
A computer-implemented method and system that processes user-provided information by assigning and updating assessment weights based on user experience, engagement, and proxy nominations, generating weighted assessments and rankings for information modules, and displaying them accordingly.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If user assessments are treated equally without weighting, then the system is simple to operate, but the ranking accuracy of information modules deteriorates
Solution Approach 1:
The system changes the parameter of user assessment weight by introducing dynamic weighting factors based on user profiles, expertise areas, and historical contribution quality. This transforms equal-weighted assessments into differentiated weighted assessments, improving ranking accuracy while managing complexity through automated calculations
Solution Approach 2:
The system introduces an intermediary weighting mechanism that mediates between raw user assessments and final rankings. This intermediary layer processes assessments through expertise matching and credibility scoring, resolving the contradiction by adding computational complexity only where needed to improve ranking precision
2Reliability
If proxy nominations are implemented to represent user expertise, then the reliability of assessments improves, but the device complexity increases
Solution Approach 1:
Proxy users serve as intermediaries between actual experts and the information module assessment system. The proxy mechanism allows experts to indirectly influence rankings through nominated representatives, improving reliability without requiring direct expert involvement in every assessment process
Solution Approach 2:
The system implements self-service through automated proxy nomination management, where users can designate proxies and the system automatically manages the weighting relationships. This reduces operational complexity while maintaining improved assessment reliability through the proxy mechanism
3Measurement precision
If dynamic weight updates are performed based on user engagement, then the ranking relevance improves, but the processing time increases
Solution Approach 1:
The system implements periodic weight updates based on user engagement metrics rather than continuous real-time updates. Assessment weights are recalculated at scheduled intervals or triggered by significant engagement thresholds, maintaining ranking relevance while reducing processing time and computational overhead
Solution Approach 2:
The system performs preliminary calculations of user assessment weights based on historical engagement data and expertise profiles. These pre-computed weights are stored and applied to assessments, reducing the time required for real-time ranking updates while maintaining high relevance
Data Source
AI summary
A computer-implemented method of processing user provided information from a plurality of users in a digital network for ranking one or more information modules is described, which involves causing at least one processor to store in memory user assessment weights, each associated with a user of the plurality of users and representing a weight to be applied to information module assessments, receive a proxy nomination message, change the user assessment weight associated with the proxy user based on the proxy nomination message, for one or more information modules: receive information module assessments, generate weighted assessments, each based on one of the information module assessments received and a user assessment weight, aggregate the weighted assessments to generate an aggregated weighted assessment, and rank a set of the plurality of information modules based at least in part on the aggregated weighted assessments. Apparatuses, systems and computer readable media also described.